Kashf Journal of Multidisciplinary Research
The Kashf Journal of Multidisciplinary Research, published by the Kashf Institute of Development & Studies, is an HEC-recognized multidisciplinary journal. It serves as a platform for scholarly exploration across various disciplines, accepting research papers that undergo a rigorous peer-review process. KJMR is a platform for scholarly exploration across various disciplines. It accepts research papers that undergo a rigorous peer-review process and publishes findings relevant to academics, policymakers, and practitioners. This journal encourages cross-pollination of ideas between different fields, potentially impacting areas like social sciences, humanities, natural sciences, and engineering. Overall, it contributes to advancing knowledge, informing practice, and stimulating innovation across a broad spectrum of disciplines. e-ISSN: 3007-200X p-ISSN: 3007-1992
KASHF INSTITUTE OF DEVELOPMENT & STUDIES
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-WEB:1a5feec1-4169-432f-8d68-057852a47104-1" data-testid="conversation-turn-4" data-scroll-anchor="true" data-turn="assistant"> The Kashf Journal of Multidisciplinary Research (KJMR) distributes all published articles under an open access license to ensure wide dissemination and reuse of scholarly work. Unless otherwise stated, articles published in KJMR are licensed under the Creative Commons Attribution License (CC BY 4.0). Under this license: Permissions Granted Readers and users are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, including commercial use Conditions of Use Appropriate credit must be given to the original author(s), the article title, and the Kashf Journal of Multidisciplinary Research. A link to the license must be provided. Any changes made to the original work must be clearly indicated. The use must not imply endorsement by the author(s) or the journal. Author Rights Authors retain full copyright of their work. Authors grant KJMR the non-exclusive right to publish, archive, and distribute the article in electronic and print forms. Authors are permitted to deposit the final published version of their article in institutional or subject repositories without embargo. Third-Party Material Any third-party content included in an article must be clearly credited and may be subject to separate licensing terms. It is the responsibility of the author(s) to obtain necessary permissions for such material. Long-Term Accessibility KJMR ensures permanent availability of published content through its official website and archiving partners, supporting long-term scholarly access. These distribution license terms align with international open access standards and support the journal’s mission to promote ethical, transparent, and wide dissemination of multidisciplinary research.
BEYOND GRADES: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ACADEMIC WRITING AND STUDENT ENGAGEMENT IN UNDERGRADUATE STUDIES AT HIGHER EDUCATION INSTITUTIONS IN BAHAWALPUR
The rapid proliferation of Generative Artificial Intelligence (GenAI) has fundamentally altered the landscape of English Language Teaching (ELT) and academic writing in higher education. This study investigates the impact of AI tools on the academic writing practices and student engagement of undergraduate students at Higher Education Institutions (HEIs) in Bahawalpur, South Punjab, Pakistan. Moving beyond grades, the research explores how these technologies influence critical thinking, learner autonomy, and the writing process itself. Grounded in Sociocultural Theory and the Technology Acceptance Model (TAM), the study adopted a sequential explanatory mixed-methods design. Quantitative data were collected through a survey of 350 undergraduate students from The Islamia University of Bahawalpur (IUB) and Government Sadiq College Women University (GSCWU), followed by semi-structured interviews with 15 students and 5 faculty members. Findings indicate that while AI tools significantly enhance writing self-efficacy and linguistic accuracy, they present a paradox for engagement. They facilitate micro-scaffolding of language skills but pose risks of cognitive disengagement and over-reliance. The study concludes with pedagogical implications for integrating AI into the Pakistani undergraduate curriculum to foster ethical, autonomous, and critical writing practices.
Copyright (c) 2026 Muhammad Aslam Khan, Tanveer Ahmed, Azuba Afzal (Author) https://creativecommons.org/licenses/by/4.0
SOURCES OF AIR POLLUTION IN RELATION TO THE PUBLIC HEALTH AND ENVOIRONMENT
Air pollution has emerged as a critical global threat to public health and the environment. Major pollutants like PM2.5, O3, NO2, and SO2 contribute to respiratory, cardiovascular, and metabolic diseases, affecting over 92% of the world’s population. Children are especially vulnerable, with pollution linked to increased morbidity and mortality worldwide. Anthropogenic activities such as vehicle emissions, power plants, and industrial processes are the major sources of air pollution, releasing harmful pollutants that impact human health and the environment. Both indoor and outdoor pollution contribute to respiratory and cardiovascular diseases, especially among vulnerable populations like children and workers. Long-term exposure to fine particles significantly increases the risk of chronic respiratory diseases like COPD and cardiovascular conditions such as ischemic heart disease. Air pollution causes climate change, acid rain, ecosystem degradation, ozone depletion, and biodiversity loss by damaging air, water, soil, and living organisms. These impacts threaten ecosystem services and global environmental health, necessitating urgent pollution control measures. Maximizing success in addressing atmospheric change and environmental degradation requires increasing public awareness, training, and engagement. A global preventative program and sustainable development strategies are essential to counteract anthropogenic air pollution and manage its health impacts. International cooperation in policy, administration, monitoring, and research is crucial, alongside advancing and synchronizing air pollution laws. Focusing on local frameworks to promote knowledge and practice can support the creation of effective global regulations for sustainable ecosystem management.
Copyright (c) 2025 Kainat Safdar, Maria Abdullah Butt, Nimra Batool, Sumaira Kanwal, Iram Batool, Kashmala Yousaf, Maria Anwar (Author) https://creativecommons.org/licenses/by/4.0
POLYUNSATURATED FATTY ACIDS (PUFAS) AND DEPRESSION IN ADOLESCENTS: A CROSS-SECTIONAL STUDY IN SOKOTO, NIGERIA
A cross-sectional study was conducted to investigate the relationship between polyunsaturated fatty acid (PUFA) intake and depression among adolescents attending Federal Neuropsychiatric Hospital Kware, Sokoto, Nigeria. Sixty participants were randomly selected and their dietary habits were assessed using a validated food frequency questionnaire (FFQ). Depression scores were evaluated using a standardized tool. Results showed that participants had low levels of PUFAs, and the common diets include palm oil being the lowest (1.65 ± 0.71%) content of PUFAs and coconut having the highest concentration (22.3 ± 8.6%). The prevalence of depression was high among participants, and it was linked to lower levels of PUFAs. The study suggests that poor dietary habits, particularly low intake of PUFAs, may contribute to depression among adolescents in Sokoto, Nigeria. This signifies the need for health education, counseling, and awareness regarding healthy dietary habits especially in adolescents.
Copyright (c) 2026 A.I. Umar, U. Abdussalam, Yusuf Yahaya Miya, Bello Buhari (Author) https://creativecommons.org/licenses/by/4.0
AN AHP-BASED MULTI-CRITERIA DECISION-SUPPORT SYSTEM FOR SUSTAINABLE CIVIL ENGINEERING APPLICATIONS: DEVELOPMENT AND CASE STUDIES
Decision-making in civil engineering often requires the careful evaluation of multiple, frequently conflicting criteria, encompassing technical performance, cost efficiency, and sustainability considerations. Conventional approaches relying on intuition or single-factor analysis are increasingly insufficient for addressing such multifaceted challenges. This study introduces an Analytic Hierarchy Process (AHP) based multi-criteria decision-support system (DSS) designed to facilitate systematic, transparent, and sustainability-focused decision-making in civil engineering contexts. The research focuses on the creation of a user-friendly, Excel-based platform that allows practitioners to implement AHP without extensive computational expertise. The DSS enables the definition of criteria and choice, execution of pairwise comparisons, consistency assessment, and final ranking through weighted aggregation. Its effectiveness is demonstrated through two real-world case studies involving sustainable concrete material selection: recycled aggregate concrete and waste tyre rubber–modified concrete. These applications integrate technical, environmental, and economic factors to identify optimal choice within a sustainability-oriented framework. Findings indicate that the proposed DSS supports objective, structured evaluation, reduces computational effort, and enhances decision transparency. The study emphasizes the significance of applied practice DDS in promoting environmentally responsible practices, offering a versatile solution for researchers, engineers, and students seeking informed, sustainable infrastructure decisions.
Copyright (c) 2025 Muhammad Talha, Mudasir Bashir, Muhammad Shameer Faisal, Abdul Saboor (Author) https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE: A FRIEND OR THREAT TO RADIOGRAPHERS?
Copyright (c) 2025 Sadia Azam, Prof. Dr. Syed Asadullah, Waqas Ahmad (Author) https://creativecommons.org/licenses/by/4.0
INTERACTIVE EFFECTS OF FOLIAR BORON AND FARMYARD MANURE ON GROWTH, YIELD, AND OIL CONTENT OF SUNFLOWER.
There was a field trial. In the fall of 2023, a study was carried out at Sindh Agriculture University Tando jam Research Area of the Students' Experimental Farm, Department of Agronomy. The purpose of the experiment was to investigate how boron administration affected sunflower plant growth, yield, and oil content. The methodology used in the experiment was a Randomized Complete Block Design (RCBD), with each treatment being replicated three times. Sunflower seeds were sown using a dibbler on ridges spaced 75 cm apart. The variety HO-1 was treated with different farmyard manure and boron foliar applications levels: T1 = 00% B + Farmyard Manure (Control), T2 = 0.5% B + Farmyard Manure @ 5 ton, T3 = 0.1% B + Farmyard Manure @ 5 ton, T4 = 0.2% B + Farmyard Manure @ 5 ton, T5 = 0.4% B + Farmyard Manure @ 5 ton. Experiment analysis of variance (ANOVA) shows significant difference (P<0.05) for all growth, yield and oil content characteristics due to treatments. Maximum plant population (m-2) (9.1), plant height (cm) (236.0 ), stem girth (cm) (10.8), head diameter (cm) (45.3), seeds head-1 (1797.3), seed weight head-1 (g) (58.6), seed index (g) (32.6), Seed yield (2183.3 kg ha-1 ) oil content of 40.00% were recorded on T5 = 0.4% B + Farmyard Manure @ 5 ton. Similarly, T4 = 0.2% B + Farmyard Manure @ 5 ton with high plant population (m-2) (8.2), plant height (cm) (231.0), stem girth (cm) (10.5), head diameter (cm) (43.3), seeds head-1 (1561.3), seed weight head-1 (g) (46.7), seed index (g) (30.0), Seed yield (1950.0 kg ha-1), oil content of 39.1%. However minimum results were observed in T1 = 00% B + Farmyard Manure (Control) high plant population (m-2) (5.8), plant height (cm) (188.3), stem girth (cm) (9.0), diameter (cm) (24.3), seeds head-1 (1139.3), seed weight head-1 (g) of (24.1), seed index (g) (21.0), Seed yield (1458.3 kg ha-1 ) the lowest mean oil content 36.2%. After going through the findings of the present research, it was concluded that the growth and yield of sunflower increased simultaneously with increasing Boron levels; and the sunflower fertilized with T5 = 0.4% B + Farmyard Manure @ 5 ton resulted in highest seed yield (2183.3 kg ha-1 ), followed by T4 = 0.2% B + Farmyard Manure @ 5 ton (1950.0 kg ha-1) and T3 = 0.1% B + Farmyard Manure @ 5 ton (1741.7 kg ha-1). It was concluded that the impact of varying boron levels on sunflower growth, yield, and oil content reveals significant correlations, emphasizing the critical role of phosphorus in optimizing sunflower productivity.
Copyright (c) 2025 Danish Manzoor, Urooj Rehmani, Asif Ali Kaleri, Saddam Hussain Mirbahar, Zaheer Ud Din, Bakhat Ali Hakro, Parvaiz Ali Kaloi, Mohammad Arsal, Abdullah Arain (Author) https://creativecommons.org/licenses/by/4.0
Hope vs. Defiance: A Comparative Corpus Analysis of Emotional Lexis in MLK’s I Have a Dream and Churchill’s We Shall Fight on the Beaches
Political speeches function not only as instruments of persuasion but also as powerful emotional artifacts that mobilize collective consciousness during moments of crisis and transformation. This study undertakes a comparative corpus-based analysis of emotional lexis in two iconic political speeches: Martin Luther King’s The American Dream and Winston Churchill’s We Shall Fight on the Beaches. Drawing on corpus linguistics and emotion-oriented discourse analysis, the research investigates how hope and defiance are linguistically constructed, foregrounded, and strategically deployed in American civil rights rhetoric and British wartime discourse respectively. Using a small, specialized corpus, the analysis employs Voyant Tools, AntConc, and Text Inspector to examine lexical frequency, keyword salience, collocation patterns, sentiment polarity, semantic fields, and evaluative language. The findings are expected to reveal significant contrasts in emotional framing: King’s discourse privileging optimism, moral aspiration, and collective uplift, while Churchill’s rhetoric emphasizes resistance, resolve, and national endurance. By integrating quantitative corpus techniques with qualitative interpretation, the study demonstrates how emotional lexis operates as an ideological and rhetorical resource in political leadership across cultures. The research contributes to corpus-assisted political discourse analysis, offering insights into the emotional mechanics of persuasion in canonical political texts
Copyright (c) 2025 Azmat Batool, Dr. M. Faisal Hanif, Mussarat Qamar, Faiza Altaf (Author) https://creativecommons.org/licenses/by/4.0
Managing User Stress Associated with Social Networking Location Disclosure: A Proposed Framework.
Social media has become the most important part of the modern society. A number of social sites have been developed which allow sharing posts, status and locations etc. thereby providing an easy access for users to see other people’s activities such as what location they are at and what they are doing. This activity engages people in social comparison in which the people compare their lives with others on the basis of information (status, location, etc.) posted by them on the social media sites. This often leads to making people think about their life as less idealized/privileged, thereby causing stress. Our study suggests that among others, one of the triggers of stress is fake location sharing by the users. So our research has proposed Framework for stress management which address issue of fake location and also allow user to share real location.it also provide about contents along with other associated information based on real user location. Usability study of framework has also been conducted.
Copyright (c) 2025 Sumaira Memon, KamranTaj Pathan, Sorath Mahar, Rafique Ahmed Vighio (Author) https://creativecommons.org/licenses/by/4.0
COMPREHENSIVE DETECTION OF MULTI-TYPE WRIST FRACTURES USING IMPROVED YOLOv8 MODEL
Bone fractures, particularly those affecting the wrists, shoulders, and arms, are common and significantly impact patient care. This study investigates the utility of YOLOv8, a deep learning model, in detecting ulna and radius fractures which are the crucial components of wrist injuries. Through collaboration with phys- iotherapists and comprehensive data collection, an annotated dataset is curated for precise wrist fracture localization. Advanced data augmentation techniques are used including Mosaic, Mix-up, and copy paste, to enhance dataset diver- sity and model robustness. Along with baseline YOLOv8 and iYOLOv8 + GC which currently reports high precision (97.2%), we also consider the most opti- mized variant. We also focus on the baseline YOLOv8 results relative to this newer version to demonstrate the focus on high precision vs the balance within recall, F1 score, and precision. The model excelled in detecting various wrist fracture types, advancing fracture detection in medical practice. The improved evaluation metrics, including accuracy, precision, recall, and F1-score, highlighted the robustness of the YOLOv8 model in identifying ulna and radius fractures. YOLOv8 achieved high scores with accuracy and precision score of 0.87 each recall 0.88, and F1-score 0.86, which indicates its proficiency in accurate fracture detection. The comparative analysis has highlighted the balanced performance of YOLOv8, as the best models like iYOLOv8 + GC also record higher values of accuracy (up to 97.2) whereas our strategy is robust in various measures. These findings highlight YOLOv8 as a promising diagnostic tool for expedited diagnosis and improved patient care in wrist injury management.
Wrist injuries; ulna and radius fractures; YOLOv8; deep learning; fracture detection
Copyright (c) 2025 Hina Mohsin, Ghazanfar Ali, Imsal Shabbir Mirza, Iqra Hameed , Tuba Younas (Author) https://creativecommons.org/licenses/by/4.0
ESTIMATION OF GENETIC VARIABILITY OF SEED TRAITS IN BREAD WHEAT
Significant genetic variability was observed among the 42 wheat genotypes for seed length and seed width, indicating the presence of substantial exploitable diversity for grain quality improvement. Analysis of variance revealed highly significant differences among genotypes for both traits. Seed length ranged from 6.445 to 8.388 mm with a mean value of 7.188 mm. Genotype PYT-2516 recorded the maximum seed length (8.388 mm), followed by PYT-2515 and PYT-2501, whereas the check cultivar NIA-Zarkhaiz exhibited the minimum seed length (6.445 mm). Most advanced lines showed superior grain length compared to the check cultivar, and several genotypes surpassed the commercial check IV-3, highlighting their breeding potential. Frequency distribution indicated that the majority of genotypes were clustered within the 6.89–7.34 mm range, while PYT-2516 formed a distinct class with the longest grains. Similarly, grain width showed significant variation among genotypes, ranging from 2.985 to 3.900 mm with an overall mean of 3.49 mm. The genotype PYT-2530 exhibited the maximum grain width (3.900 mm), followed by PYT-2506 (3.700 mm), whereas PYT-2515 recorded the narrowest grains (2.985 mm). Most genotypes were concentrated within the 3.585–3.785 mm class, while PYT-2530 represented the boldest grain class. The wide variation in both grain length and width suggests strong genetic control and provides valuable opportunities for selection and hybridization aimed at improving grain size, grain weight, and overall wheat productivity.
Copyright (c) 2025 Ali Abbas Kaleri, Parvaiz ali kaloi, Asad Ali korai, Saddam Hussain Mirbahar, Shahzad Ali Majeedano, Abdul Qayoom Majeedano, Murk Keerio, Shagufta Baladi, Tameer Hyder Shah, Sarang Ali Bhand (Author) https://creativecommons.org/licenses/by/4.0
INVESTIGATING CIRCULAR ECONOMY BARRIERS IN NOWSHERA’S MARBLE FACTORIES
The marble industry in District Nowshera, Khyber Pakhtunkhwa, plays a vital role in Pakistan’s economy but remains heavily dependent on a linear production model, resulting in excessive waste generation, high energy consumption, and environmental degradation. This study investigates the barriers to adopting circular economy (CE) practices in the marble sector, focusing on Shaidu, Jehangira, and Khairabad. A quantitative research design was employed, incorporating two structured surveys: one targeting CE experts and professionals (n = 64) and another engaging marble factory managers (n = 10). Data were analyzed through descriptive statistics, weighted averages, cross-tabulations, and correlation analysis to capture both systemic and operational perspectives. Findings reveal that weak regulatory enforcement, high electricity costs, lack of financial incentives, minimal awareness, and inadequate technological infrastructure are the principal barriers to CE adoption. Notably, 90% of factory managers had no prior awareness of CE, yet all expressed willingness to adopt sustainable practices when informed. The study concludes that integrated policy reforms, financial support, awareness campaigns, and technological investments are essential for transitioning Pakistan’s marble industry toward circular models aligned with sustainable development goals.
Copyright (c) 2025 Hani Alam, Hafiz Muhammad Usman Khan (Author) https://creativecommons.org/licenses/by/4.0
COMPUTATIONAL ANALYSIS OF TURBULENT FLOW PHENOMENA IN BAFFLED RECTANGULAR CHANNELS: SINGLE VS. DOUBLE STEP CONFIGURATIONS
The present work shows computational analysis of fluid flow through a rectangular channel modified by single as well as double baffle steps utilizing the finite element method (FEM) within the COMSOL Multi-physics background. The study focuses on the complex relationship of boundary layer separation, vortex generation, and flow phenomena governed by the incompressible Navier-Stokes and continuity equations. To achieve a numerical solution, these second-order governing equations were converted into a first-order system by introducing vorticity as an additional unknown. Discretization was performed using the Galerkin finite element method coupled with a least squares residual approach to manage non-linearity and higher-order terms, while the Newton-Raphson method was utilized for final calculations. Steady-state simulations conducted across a Reynolds number (Re) range of 1 to 500 reveal that vortex formation is strongly dependent on fluid inertia. Results indicate that while no vortices appear at Re = 1, distinct primary and secondary vortices begin to form in the corners of the step baffles by Re = 50. As Re increases to 200, these vortices merge, and by Re = 500, the consolidated secondary vortex expands significantly to fill the entire channel domain. Comparative analysis between single- and double-baffle configurations shows that increasing the number of baffles facilitates more energetic and sustained vortex growth, with the double-step geometry reaching a maximum vortex length of approximately 0.20 compared to 0.06 in the single-step design at Re = 500. The numerical results demonstrate strong agreement with established literature, confirming the accuracy of the proposed computational approach for investigating flow efficiency and energy dissipation in baffled channels.
Copyright (c) 2025 Akhalque Ahmed Abbasi, Murtaza Hussain Shar, Mazhar Ali Sahito, Dr. Abdul Raheem Shar (Author) https://creativecommons.org/licenses/by/4.0
ECOLOGICAL AND SOCIOECONOMIC CONSEQUENCES OF CORAL REEF DEGRADATION
The coral reefs represent some of the planet's most significant ecological systems for marine biodiversity, along with environmental benefits. Still, they are rapidly vanishing due to a combination of various environmental and human-caused stressors. Global climatic changes, predation, coral diseases, sedimentation, marine development, and unsustainable fishing activities are major causes of reef degradation. Furthermore, declining water quality caused by eutrophication, plastic debris, and chemical pollutants, especially polycyclic aromatic hydrocarbons, UV filters, and hydrocarbons, worsens the situation by harming coral health and reproduction. The review examines the ecological and socioeconomic consequences of reef decline, including reduced ecosystem services, loss of marine habitats, declining fish populations, and diminished coastal protection and tourism revenue, particularly in regions that rely on reefs for their livelihoods. Several active restoration methods have been examined, including coral gardening, larval propagation, substrate stabilization, micro-algal removal, micro-fragmentation, artificial reefs, and coral transplantation, alongside passive approaches like establishing marine reserves and predator exclusion, highlighting the urgent need for comprehensive and multifaceted strategies. The paper offers several recommendations, including improving monitoring tools, expanding restoration efforts, enhancing stakeholder collaboration, and developing legislation to safeguard reefs for future generations. Through both national and global initiatives, it remains possible to prevent further deterioration and conserve the vital services they provide.
Copyright (c) 2025 Maria Imran, Maria Abdullah Butt, Iram Batool, Sumaira Kanwal, Hassan Zeb, Asma Majeed (Author) https://creativecommons.org/licenses/by/4.0
MENTAL WELLNESS IN THE POST-COVID ERA: CHALLENGES FACED BY UNIVERSITY STUDENTS IN KARACHI
The COVID-19 pandemic has profoundly affected mental wellness worldwide, with university students representing a particularly vulnerable population. In Karachi, Pakistan’s largest metropolitan city, students experienced abrupt academic disruptions, prolonged social isolation, and uncertainty regarding health and career prospects. This review aims to synthesize available literature on mental wellness challenges faced by university students in Karachi following the COVID-19 pandemic. Peer-reviewed studies published between 2020 and 2025 were reviewed to assess the prevalence of depression, anxiety, stress, and related psychosocial issues. Findings indicate a substantial rise in psychological distress among students, particularly females and those enrolled in medical and health sciences programs. Academic stress associated with online learning, fear of infection, reduced social interaction, and financial instability emerged as major contributing factors. The review highlights the persistent impact of the pandemic on student mental health and underscores the urgent need for institutional mental health services, preventive strategies, and policy-level interventions to promote student well-being in post-pandemic academic settings
Copyright (c) 2025 Sarah Jameel Khan, Hasan Tahir, Abeer Jameel Khan (Author) https://creativecommons.org/licenses/by/4.0
OPERATIONAL ANDROID MALWARE FILTERING: CALIBRATED PROBABILITIES AND DISTRIBUTION-FREE GUARANTEES
Objectives— We establish and rigorously evaluate an Android malware detector that optimizes not only discrimination (how well samples are ranked) but also reliability: (i) probabilities that correspond to empirical risks and (ii) explicit, distribution free uncertainty quantification. Our goals are to (1) benchmark strong tabular baselines on a canonical CSV corpus; (2) calibrate scores so a value like 0.9 aligns with “roughly 90% malicious”; and (3) add inductive conformal prediction (ICP) to provide finite-sample coverage guarantees and a principled abstain option. Methodology— The pipeline: (i) ingest and sanitize a dataset of 15,036 applications with static features; (ii) encode mixed datatypes via one-hot and factorization; (iii) train Logistic Regression (LR), Random Forest (RF), and XGBoost (GBDT); (iv) report ROC–AUC, PR–AUC, F1, Accuracy, Brier score; (v) apply post hoc calibration (Platt, Isotonic) and compute Expected Calibration Error (ECE) with reliability diagrams including Wilson intervals; and (vi) deploy ICP to return prediction sets achieving target error α ∈ {0.10,0.05,0.01}, yielding selective automation. Dataset— A text-only (CSV) Android corpus of static attributes (manifest/permissions/API-like proxies) with a binary label (benign/malicious). Preprocessing drops identifiers, coerces types, imputes missing values, applies de-duplication, and uses an 80/20 stratified split with a 20% calibration carve-out from training (effective 64/16/20 train/cal/test). Results— RF/GBDT provide the best ranking. Calibration consistently lowers ECE/Brier and aligns reliability curves with the identity line; we visualize the effect for RF. ICP tracks the nominal target coverage closely with average set size near one, indicating selective abstentions. We include a temporal slice analysis to illustrate robustness under modest distribution shift, plus an operational thresholding example linking (α,t) to analyst load. Applications— Calibrated probabilities support auditable thresholds in app stores and enterprise gateways; ICP provides a knob for predictable abstentions under finite-sample guarantees. The approach is model-agnostic, simple to reproduce, and easy to retrofit in static or hybrid pipelines. Availability— Scripts for preprocessing, calibration, ICP, metric computation, and figure generation, along with CSV/PNG artifacts referenced in the paper, are provided in the artifact bundle.
Copyright (c) 2025 Basit Raza Dharejo, Prof. Dr. Abdullah Maitlo, Zahid Hussain Shar, Iqra Hyder (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING FAT-CAT MODEL OF AI ADOPTION IN AI-ENABLED RECRUITMENT: UNDERSTANDING THE MODERATING ROLE OF AUSTRALIAN RECRUITERS’ PERCEPTION
Recruitment is being transformed by incorporation of AI, which brings efficiency however also concerns the algorithms’ transparency. The effects of AI's FAT-CAT model (explain-ability and augmentation capabilities) on teamwork, hiring, and morality in the Australian hiring process are the focus of this study. Quantitative data was researched and analyzed. To gather data, questionnaire was sent out to various human resources managers and recruiters in various industries in Australia. With a 5% margin of error, the sample size was 178 out of a population of 300. This study used snowball sampling. SPSS was used to analyze data to determine how explain-ability, augmentation, and human-AI adoption affect Australian recruitment. Moreover, moderation of individual perception was also investigated. Findings suggests that explain-ability characteristics marginally affect AI adoption, whereas augmentation features greatly do. AI's promise and problems in recruitment need user-friendly, bias-aware tools and continual recruiter coaching, according to the report. Ensure explain-ability, openness, and empower recruiters with continuing training. Individual perception moderates the association of explainability and augmentation features of AI adoption among Australian recruiters. AI should boost recruiters' skills without replacing human judgement, preserving their crucial position in talent acquisition.
Copyright (c) 2025 Dr. Irram Shahzadi, Umair Ali, Fareeda Riaz (Author) https://creativecommons.org/licenses/by/4.0
A HYBRID DATASET-BASED ENSEMBLE STRATEGY FOR EFFICIENT BREAST CANCER DETECTION
Breast cancer is especially dangerous for women because it kills and hurts a lot of people. Because of this, there is a need for an algorithm that can spot the first signs of breast cancer essential. One of the most common types of cancer in women is breast cancer. Its spread among people is a major worry all over the world. To save the patient's life, it is very important to find the disease and treat it right away. Last year, more than 2.3 million women were told they had breast cancer, and about 0.7 million of them died. Manually diagnosing the disease isn't very good, and most of the time, it's almost impossible to find severe cancer early, which means that the patients die. Machine learning is a key part of figuring out what early signs of breast cancer to look for. In this paper Machine Learning (ML) and Deep Learning (DL) methods can be used to find breast cancer in early stage and accurately to save lives. In this work breast cancer predicted by using a hybrid model to combine ML and DL techniques. Firstly, a DL method Scale-Invariant Feature Transform (SWIFT) is used to extract the meaning full information from the image dataset and then different machine learning models such as (K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM)) are applied to classify the malignant and benign cases. Secondly, these models classified the instances into two classes. The main focus of this work is to figure out whether a breast cancer is benign or malignant based on different specific characteristics taken from a group of images from normal and breast cancer patients. The results of contracted ML models compared on the basis of accuracy, precision, Recall and F1-Score. This study used Principal Component Analysis to find the optimized features from the set of extracted features for improving the accuracy of the used models. This is done by using multiple Machine Learning algorithms and choosing the classification model with the highest accuracy.
Copyright (c) 2025 Muhammad Sajid Maqbool, Nosheen Fatima, Rubaina Nazeer, Naeem Aslam, Faisal Abbas , Unaiza Sumra, Muqadas Nadeem (Author) https://creativecommons.org/licenses/by/4.0
EVALUATION OF WATER QUALITY AND HUMAN HEALTH RISK ASSESSMENT OF DRINKING WATER OF TALUKA BADIN, SINDH
A total of 25 drinking water samples were collected in triplicate from six union councils of Taluka Badin. Various parameters were determined using standard laboratory methods. Parameters include pH, EC, TDS, chlorides, alkalinity, As, Cd, Cr, Co, Cu, Fe, Mn, Ni and Zn. Heavy metals were analysed using an atomic absorption spectrophotometer. The concentration ranges of heavy metals As, Cd, Cr, Co, Cu, Fe, Mn, Ni and Zn are given as 0.00–0.0145, 0.001–0.012, 0.013–0.040, 0.001–0.014, 0.056–0.329, 0.085–0.156, 0.030–0.150, 0.001–0.106 and 0.060–1.00 mg/L, respectively. To assess human health risk, the chronic daily intake (CDI), hazard quotient (HQ), hazard index (HI) and cancer risks were calculated. Non-carcinogenic risk was calculated by CDI, HQ, & HI, whereas carcinogenic risk was measured for arsenic, chromium, and nickel. Based on the findings, the drinking water in the coastal areas of Taluka Badin is contaminated with heavy metals, presenting a potential health risk to the population. The calculated cancer risk and hazard indices indicate the need for measures to improve drinking water quality in the region.
Copyright (c) 2025 Abdul Raheem Shar, Prof. Dr. Ghulam Qadir Shar, Rubina Naz Mirani, Tahmina Fakhr-u-Nisa Abbasi, Sahib Ghanghro, Rabia Parveen Memon, Dr. Noorul Hassan Shar, Farzana Mangrio (Author) https://creativecommons.org/licenses/by/4.0
ANTIHYPERTENSIVE EFFECT OF HERBAL FORMULATION: WITH POSSIBLE MECHANISM OF ACTION.
INTRODUCTION: This research work was done to evaluate the in vivo hypotensive and antihypertensive potential of polyherbal product Garlina and determine its receptor activity. Garlina was evaluated for lowering blood pressure in anaesthetized Wistar rats by using BP transducer coupled with Power Lab data acquisition system. METHODOLGY: Intravenous administration of Garlina produced hypotensive effect in dose-dependent manner (29- 61.1) % fall in MABP at the log doses of 1, 3, 5, 10 and 30 mg/kg in normotensive rats. RESULTS: The hypotensive effect of Garlina (10mg/kg) was abolished when rats pretreated with atropine sulfate. The same dose of Garlina did not eliminate the hypertensive effect of Phenylephrine (10-4 M) but it caused significant fall in MABP when given prior to adrenaline (Adr, 1µg/kg). These data showed that Garlina possess blood pressure lowering activity mediated via muscarinic receptors as well as β- adrenergic pathways. CONCLUSION: Following the L- NAME administration, Garlina gradually dropped the MABP in 15 minutes. The obtained results indicate that the possible mechanism of action is NO synthesis and NO-cGMP pathway behind the antihypertensive and vasorelaxant effect of Garlina. Therefore it is concluded that provides a rationale to the medicinal use in hypertension.
Copyright (c) 2025 Dr. Farzana Sadaf , Dr. Sarah Jameel Khan, Ms. Hina Ilyas, Prof. Dr. Sumbul Shamim (Author) https://creativecommons.org/licenses/by/4.0
FASHION AND SUSTAINABILITY: TOWARDS A GREENER FUTURE
Copyright (c) 2025 Umbreen Kanwal Dogar, Shahid Zaheer (Author) https://creativecommons.org/licenses/by/4.0
AWARENESS OF RISK FACTORS FOR BREAST CANCER, SELF-BREAST EXAMINATION AND ITS ASSOCIATED FACTORS IN SKARDU, GILGIT BALTISTAN: A COMMUNITY BASED SURVEY
Background: Breast cancer (BC) is a leading cause of cancer-related morbidity and mortality among women in Pakistan, with one in nine at lifetime risk. Early detection through breast self-examination (BSE) is critical, yet its practice depends on adequate awareness of risk factors, which remains poorly characterized in rural regions like Skardu, Gilgit-Baltistan. Objective: This study assessed the awareness of BC risk factors and BSE practices and identified associated sociodemographic factors among women aged ≥15 years in Skardu. Methods: A community-based cross-sectional survey was conducted from November 2022 to June 2023. Using multistage stratified random sampling, 412 women were enrolled. Data were collected via a structured questionnaire assessing sociodemographic, medical history, and awareness of BC risk factors and BSE. Descriptive statistics and chi-square tests were used for analysis. Results: Awareness was critically low: 73.8% (n=304) could not name a familial risk factor, and only 4.6% (n=19) knew the correct BSE procedure. High awareness of BC risk factors was significantly associated with higher perceived income (p=0.051), personal history of BC (p=0.044), and contraceptive type (p=0.007). Awareness of BSE was linked to marital status, education level, breastfeeding history, and family history of BC (p<0.05). Conclusion: There is a profound deficit in knowledge regarding BC risk factors and BSE among women in Skardu. Targeted, culturally sensitive awareness campaigns and educational interventions are urgently needed to promote early detection practices in this region.
Copyright (c) 2025 Nosheen Niaz, Hasina Niaz, Norina Niaz, Samina Anwaar, Sheraz Farzd (Author) https://creativecommons.org/licenses/by/4.0
AN ANALYSIS OF THE EXISTENTIAL SIGNIFICANCE OF WOMAN IN NAJEEBA ARIF’S SHORT STORY “PICHHLI RAAT KA JADOO"
Najeeba Arif, an eminent Urdu writer, critic, and academic, is widely known for addressing existential and gender-based concerns in her creative and critical works. This paper offers a critical reading of her short story “Pichhli Raat ka Jadoo”, with particular focus on the suppressed desires and existential condition of its female protagonist. Unlike conventional portrayals of women suffering from material deprivation, this story depicts a character whose economic needs are met but whose emotional and conjugal aspirations are systematically denied by her husband. This persistent negation gradually compels her to silence her own longings, leading to a slow existential suffocation and eventual symbolic death. Employing Martha C. Nussbaum’s Objectification Theory, the study examines how patriarchal dynamics strip the woman of subjectivity and reduce her to an object. At the same time, the analysis foregrounds the writer’s subtle critique of this reduction, highlighting her reassertion of female agency and existential significance. By situating the narrative within the broader framework of contemporary Urdu fiction and feminist literary discourse, this research underscores the writer’s vital contribution to reshaping representations of women in literature.
Copyright (c) 2025 Dr. Aqlima Naz (Author) https://creativecommons.org/licenses/by/4.0
UTERINE FACTOR INFERTILITY: A MAJOR ISSUE OF THE PRESENT AGE
Uterine Factor Infertility (UFI) represents a definitive and challenging subtype of female infertility, characterized by the uterus itself being the principal barrier to gestation. It encompasses Absolute UFI (AUFI), involving anatomical absence, and Non-Absolute UFI (NAUFI), involving a nonfunctional uterus. This systematic review synthesizes current evidence on the epidemiology, etiologies, and treatment landscape of UFI. Despite its clinical significance, reliable population-based and age-stratified prevalence data, particularly for women under 40, remain scarce. A key finding is the variable prevalence of hysterectomy—a major cause of AUFI—ranging from 1.70% to 10.0% in international studies. Congenital anomalies, intrauterine pathologies, and iatrogenic damage constitute other primary causes. For women with AUFI, traditional paths to biological parenthood are limited to adoption or surrogacy. Uterus transplantation (UTx) has emerged as a pioneering, albeit experimental, treatment offering the potential for gestation, with over 50 live births reported globally. However, significant surgical, immunosuppressive, and ethical challenges persist. This review underscores the urgent need for robust epidemiological studies to guide resource allocation and highlights UTx as a dynamic frontier in restoring reproductive potential for this patient population.
Copyright (c) 2025 Sana Bahadar, Amir Nawaz, Hilal Ahmad Malik (Author) https://creativecommons.org/licenses/by/4.0
ENHANCING CLINICAL APPLICABILITY OF CNN MODELS FOR PNEUMONIA DETECTION IN CHEST X-RAYS
In order to lower the mortality rates linked to pneumonia which continues to be a major danger to global health quick and precise diagnostic tools are required. Deep learning combined with chest X-ray imaging has become a revolutionary method for automatic pneumonia identification. By carefully assessing and refining four well-known architectures AlexNet, ResNet18, DenseNet201 and SqueezeNet this study aims to improve the clinical usability of convolutional neural network models. In order to replicate real-world clinical limitations, we used transfer learning and trained each model only on CPU-based hardware using a three-class chest X-ray dataset of 5,863 pictures classified into normal, bacterial pneumonia and viral pneumonia. Classification accuracy computing efficiency and deployment feasibility in resource-constrained environments were used to evaluate performance. Our results show that while all models achieve impressive accuracy ResNet18 and DenseNet201 are especially well-suited for real-world clinical implementation without requiring expensive GPU infrastructure because they strike the ideal balance between diagnostic precision and computational feasibility. In order to enable efficient and trustworthy pneumonia detection in a variety of healthcare settings this study offers a useful and comparative approach for CNN model selection and optimization.
Copyright (c) 2025 Sabeen Fatima Majid, Dr. Hira Zahid, Nabeeha Sahar, Hamza Khan, Shahzad Nasim (Author) https://creativecommons.org/licenses/by/4.0
DETERMINANTS OF CROWDFUNDING PARTICIPATION: THE INTERPLAY OF PERCEIVED USEFULNESS, TECHNOLOGY READINESS, AND FINANCIAL LITERACY
This study investigates the determinants of crowdfunding in Pakistan through an integrated moderating and mediating model. In this study, trust in crowdfunding serves as a mediating variable, while financial literacy acts as a moderating variable. Data were collected from 314 respondents using a structured online questionnaire distributed to entrepreneurs, small business owners, and digital finance users. The data were analyzed using SPSS and SmartPLS, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for measurement and structural analysis. Results reveal that smart device usage readiness and digital financial services significantly influence crowdfunding adoption, while perceived usefulness and perceived ease of use were not significant predictors. Trust in crowdfunding mediated the relationships between smart device readiness, technology readiness, and digital financial services with adoption, whereas financial literacy moderated only the link between digital financial services and adoption. The findings highlight that infrastructural and trust factors outweigh traditional technology acceptance constructs in shaping crowdfunding behavior in developing contexts. The study contributes to the theoretical refinement of TAM and UTAUT in FinTech research and offers practical implications for policymakers and platform developers aiming to enhance digital trust, financial literacy, and inclusive crowdfunding ecosystems in Pakistan.
Copyright (c) 2025 Muhammad Yasir Tahir, Muhammad Sadiq Shahid, Khawar Naheed (Author) https://creativecommons.org/licenses/by/4.0
AI-INTEGRATED IoT SYSTEM USING MACHINE LEARNING TECHNIQUES FOR CONTINUOUS HEALTH TRACKING AND PREDICTIVE PATIENT MONITORING
The combination of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming modern healthcare, especially in the area of remote patient monitoring and disease management. This study focuses on designing and improving an intelligent remote health-monitoring framework that utilizes IoT-based sensing devices together with machine learning techniques. The aim is to enhance diagnostic accuracy, enable real-time health data processing, ensure secure data transmission, and support seamless system integration to improve patient outcomes. To overcome key challenges—including unreliable data, privacy risks, and limited computational resources—a novel and efficient monitoring approach is presented. Multiple machine learning models, such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), are applied to analyze vital parameters like heart rate, blood pressure, and ECG signals. Experimental results show that the Random Forest classifier delivers superior performance across accuracy, precision, and recall metrics, making it well suited for real-time healthcare applications. Overall, this research contributes practical insights into AI-driven remote health systems and sets a strong foundation for future advancements in personalized, scalable, and cost-effective healthcare technologies.
Copyright (c) 2025 Muhammad Awais Khan , Haider Ali Arshad, Muhammad Tanveer Meeran, Muhammad Faisal Sohail, Salahuddin (Author) https://creativecommons.org/licenses/by/4.0
DEEP SEMANTIC INTELLIGENCE FOR TWITTER SPAM DETECTION USING LATENT SEMANTIC ANALYSIS
Social media platforms, particularly Twitter, have become integral to global communication, enabling users to share information instantly with large audiences. However, Twitter’s growing popularity has attracted malicious actors who spread misinformation, phishing attempts, and other spam content. This paper introduces a novel hybrid approach that combines Latent Semantic Analysis (LSA) with traditional machine learning classifiers to effectively distinguish between legitimate and spam tweets. We collected and processed over 5.5 million tweets using Twitter’s API, extracted key features using a statistically validated LSA technique, and implemented four supervised learning algorithms: Naïve Bayes, Support Vector Machine, Decision Tree, and Logistic Regression. The experiments were conducted using rigorous 10-fold cross-validation, and models were evaluated based on accuracy, precision, recall, and F1-score. Our LSA-enhanced approach demonstrated significant performance improvements over traditional methods, with the Naïve Bayes classifier achieving 96.82% accuracy, representing a 5.49% improvement over baseline techniques. Additional error analysis revealed that our approach is particularly effective at identifying evolving spam patterns involving promotional content and malicious URLs.
Copyright (c) 2025 Muhammad Haroon, Shakeeb A. Khan, Muhammad Umair, Muhammad Abrar, Shoaib Ali Qureshi (Author) https://creativecommons.org/licenses/by/4.0
Unified Computational Intelligent Framework for Medical Image Smart Retrieval using Hybrid Feature Modeling
Over the past few decades, Content-Based Image Retrieval (CBIR) has emerged as one of the most prominent research areas within the domain of computer vision. With the continuous growth of visual data and the rapid expansion of the internet, there is a growing demand for retrieval strategies that go beyond simple text-based methods and provide more relevant, user-oriented access to multimedia content. Various systems and tools have been developed to facilitate efficient querying and interaction with large-scale audio-visual databases. However, significant challenges remain, particularly when dealing with massive and diverse image repositories. In the medical field, digital imaging plays a crucial role, as images are generated from various instruments and modalities. The identification of image modalities is a critical step that helps narrow down the scope of analysis and enhances the precision of search results. The objective of this research is to design a well-structured classification and retrieval framework capable of categorizing medical images based on their respective modalities. The proposed approach focuses on modality-based classification and retrieval of medical images. Experiments were conducted using the dataset employed for the ImageCLEF2012 modality classification task. The classification process relies on visual features extracted from each image, including Scale-Invariant Feature Transform (SIFT) , Local Binary Pattern (LBP) , Local Ternary Pattern (LTP) , Edge Histogram Descriptor (EHD) , Color and Edge Directivity Descriptor (CEDD) , Color Edge Detector using Wavelet Transform , and Color Histogram . These features are subsequently combined into a single composite feature vector, which serves as input to a Support Vector Machine (SVM) classifier with a chi-square kernel for categorization into 31 distinct modality classes. The proposed framework achieved an overall classification accuracy of 72.2%, which represents a 2.6% improvement over the highest accuracy previously reported in the ImageCLEF2012 competition using visual feature-based methods.
Digital Image, Machine Learning, Edge Histogram Descriptor, Scale-Invariant Feature transform
Copyright (c) 2025 Muhammad Awais Khan, Hamza Arif, Haider Ali Arshad, Hafiz Muhammad Ijaz, Muhammad Tanveer Meeran (Author) https://creativecommons.org/licenses/by/4.0
COMPARATIVE MOLECULAR CHARACTERIZATION OF PLEOMORPHIC ADENOMA AND CARCINOMA EX-PLEOMORPHIC ADENOMA
Introduction: Salivary gland tumours can be categorized as either benign or malignant based on histopathology. Benign tumors comprise about two-thirds of all neoplasms of the salivary glands. The most common type is pleomorphic adenoma. Malignant tumors are less common but biologically different and clinically significant. A malignant epithelial tumour recognized as carcinoma ex pleomorphic adenoma (Ca ex PA) develops from an underlying pleomorphic adenoma (PA), signifying a change from benign to malignant neoplasia in the salivary glands. The diagnosis of these tumours have been completely transformed by molecular techniques. Objective: The objective of this study was to characterize the specific genetic alterations that differentiate pleomorphic adenoma (PA) from carcinoma ex-pleomorphic adenoma (Ca ex PA) using NGS. Material And Methods: This study was conducted at Institute of Pathology and diagnostic Medicine KMU, Shaukat Khanum Memorial Cancer Hospital Lahore and Lab Genetics Lahore. Four patients of both genders were selected through Non-probability convenience sampling technique. All patients included were diagnosed with Pleomorphic Adenoma and Carcinoma-ex-Pleomorphic Adenoma of salivary gland. Bioinformatics analysis were performed on a Linux computer cluster. Results: In this study 4 cases of salivary gland tumors (2 benign pleomorphic adenomas and 2 malignant carcinoma ex-pleomorphic adenoma, Ca ex PA) were analyzed using clinical data and next-generation sequencing (NGS) to investigate the molecular drivers of benign and malignant transformation. Recurrent PLAG1 activation emerged as a central oncogenic driver across four salivary gland tumors, involving PLAG1–CTNNB1 and PLAG1–LIFR fusions in pleomorphic adenomas, each leading to constitutive PLAG1 overexpression. In contrast, carcinoma ex-pleomorphic adenomas demonstrated greater genomic complexity, including PLAG1 rearrangements or PLAG1–TCEA1 fusion accompanied by HMG2 copy-number gains. These findings suggest that while PLAG1-driven fusions initiate tumorigenesis, secondary alterations such as HMG2 amplification promote malignant transformation and increased tumor aggressiveness. Conclusions: PLAG1 gene fusions (with CTNNB1, LIFR) are key molecular events leading to pleomorphic adenoma formation. Progression to carcinoma ex-pleomorphic adenoma occurs when PLAG1 or LIFR fusions are accompanied by HMG2 gene gain, indicating dual oncogenic activation.
carcinoma ex-pleomorphic adenoma (Ca ex PA)
Copyright (c) 2025 Mashooq Khan, Dr Bibi Maryam, Abdul Malik, Abdullah, Waqas Ahmad, Arsalan Waqas Ahmad Shah, Dr Zia Ur Rehman Farooqi (Author) https://creativecommons.org/licenses/by/4.0
EMERGENT COLLECTIVE BEHAVIOR: PROBING THE DENSITY DEPENDENCE WITH THE VICSEK MODEL
The Vicsek model provides as a primary outline for studying the emergence of collective motion or “flocking” from simple limited interaction rules among self – propelled particles. This work examines the particular effect of particle density on the collective behaviour and commanding within such an organization. Simulations were carried out using FORTRAN program in a Linux atmosphere, with parameters including particle speed, noise and interaction radius held constant whereas carefully varying the number of particles (N) from 10 to 2000 within a fixed spatial box. The normalized average velocity (va) was used for quantifying the degree of collective motion. The results reveal apparent and non-monotonic reliance behaviour on particle density. Particles move independently with minimal consistency at very low densities. By increasing density, transitions takes place where particles begin to form coherently moving clusters. Although, at intermediary densities, group formation can lead to a decrease in universal order if groups move in dissimilar directions. At extremely high densities, a high degree of global alignment is constantly covered, with the system possessing strong polar order (va ≈ 0.999). The present work concludes that while enhancing density usually promotes collective motion, the relation is complicated, with optimal global coordination emerging at sufficiently high densities where local interactions percolate throughout the complete system.
Copyright (c) 2025 Murtaza Hussain Shar, Dr. Israr Ahmed Memon, Dr. Abdul Rahim Shar, Mr. Mazhar Ali Sahito (Author) https://creativecommons.org/licenses/by/4.0